tbuckley/cabot-search huggingface dataset:
HR+/HER2- Breast cancer: The effect of online education on clinician knowledge, competence, and confidence in monitoring, mitigating toxicities, and developing patient-centric plans using a team-based approach.
Authors: Urvi Patel, Nabil Dorkhom, Victoria Phoenix, Jacob Cohen
Journal: Journal of Clinical Oncology (2023)

e12522 Background: CDK4/6 inhibitors provide effective treatment options for patients with HR-positive, HER2-negative breast cancer (BC), but adherence and persistence can be challenging. To address this gap, a global educational activity was developed that consisted of a 2-hour live streaming virtual meeting with interactive workshops and a 45-minute online enduring activity. The objective of this study was to assess the educational impact of the online enduring educational activity. Methods: To assess educational impact a repeated-pairs pre-/post-assessment study design using 3 knowledge/competence questions, in which everyone served as their own control. A paired samples t-test assessed mean differences in average number of correct responses pre- to post-assessment, and a McNemar’s test assessed significance of improvement in single questions from pre- to post-assessment. P values &lt; .05 are statistically significant. Confidence was also assessed using a pre/post confidence question. The activity launched on June 4, 2021, and data was collected till March 30, 2022. Results: 80 oncologists from outside the US (OUS) and 69 oncologists from the US who answered all the assessment questions were included in the analysis. Each question assessed a learning objective. All three knowledge/competence learning objectives showed a significant improvement in the OUS oncologist group, and 2 of the 3 showed a significant improvement in the US group. Conclusions: This online enduring activity resulted in a significant improvement of oncologists’ knowledge and competence related to clinical evidence for the use of CDK4/6i, managing adverse events associated with CDK4/6i and monitoring patients on CDK4/6i. Confidence of oncologists in counselling patients on the importance of adherence to CDK 4/6 inhibitor therapy also improved in 29% of OUS oncologists and 33% of US oncologists. However, there is still a need for education in a significant percentage of both OUS and US oncologists on this topic. [Table: see text]
Deriving breast cancer chemotherapy patterns from real-world data.
Authors: Jack W. London, Julia ORourke, Jeff Warnick, John Doole, Luc De Keyser, Zuzanna Drebert, Olivia Wan, Courtney Thompson, Matvey Palchuk
Journal: Journal of Clinical Oncology (2023)

e13586 Background: Real World Data (RWD) collected during routine medical practice can help in clinical trial design, planning and execution. In addition, it can provide a compelling picture of safety of medical products, and account for all potential adverse events that can be encountered in routine clinical practice. However, raw drug information about chemotherapy in cancer care is not readily understandable in the format it is collected. Analytical techniques need to be applied to extract regimen information, which includes drugs, dosage, number of cycles and cycle length. Methods: A retrospective study was performed using data on 7,798 breast cancer patients from the TriNetX Network, a federated network of de-identified, HIPAA-compliant, health data from 21 healthcare organizations across North America as of May 2022. We investigated a method built on rule-based algorithm and clustering analysis to extract regimens and their patterns of administration and align them into lines of treatment (LOT). To derive drug administration patterns, we clustered administration time periods using three features: total number of drug administrations, median number of days between drug administrations, and standard deviation of the days between drug administrations. Results: The patterns of administration that correspond to the two most common regimens for Erb-B2 receptor tyrosine kinase 2 negative group (ERBB2-) stages 1, 2 and 3 are shown. We looked at patients with Hormone Receptor (HR+) positive and Triple Negative (TN) breast cancer. Results of our analysis were in a close agreement with NCCN Guidelines. However, regimen administration patterns varied. This information can be useful to characterize patients based on the adherence to the expected chemotherapy regimens. It can also provide meaningful insight into burden of illness, such that patients who have higher variability of the drug administration might be faced with clinical problems such as drug tolerability or side effects. The variability might also signify access to care challenges. Conclusions: Understanding regimen LOT and administration patterns is central to research based on RWD, but it can also be useful for clinical trial design, execution, as well as site selection and patient recruitment for clinical trials. [Table: see text]
Performance analysis of four machine learning algorithms for the accurate prediction of metastatic disease in cutaneous squamous cell carcinoma.
Authors: Tom William Andrew, Iakov Bolnykh, Amy Louise Bowes, Suhari Arahliya Serendibsha Grace Fernando, Ashvati Nair, Sabrina Noor Pia Martin, Balraj Maan, Philip Sloan, Penny Lovat, Aidan Rose
Journal: Journal of Clinical Oncology (2023)

e13579 Background: Cutaneous squamous cell carcinoma (cSCC) are the most common form of metastasising skin cancer. Whilst rates of metastatic cSCC are low, they account for a significant proportion of skin cancer related morbidity and mortality, particularly within elderly cohorts, which poses a significant burden to healthcare services. Established cSCC tumour staging systems perform poorly at predicting metastatic risk. Additionally, we lack clinically validated prognostic biomarkers – highlighting the unmet need for novel risk stratification tools to guide clinical practice and improve outcomes for patients with advanced disease. We aimed to train four recognised machine learning (ML) algorithms on a large clinic-pathological dataset of primary cSCC, with the objective of optimising an ML strategy and developing a reliable and clinically useful risk stratification tool capable of accurately predicting metastatic events following primary cSCC. Methods: A dataset of primary cSCC registrations was derived from the Northern Cancer Registry, UK. This identified 7003 histologically confirmed primary cSCC registered between 2010–2020; providing a minimum of 2 years clinical follow-up. We conducted a retrospective analysis of standardised pathology datasets, recording clinical-pathological features. Primary outcome measure was regional and/or distant metastasis. Four machine learning algorithms, were trained based on these features, including: a Logistic Regression Trainer, a Decision Tree Classifier, a Random Forest Classifier and a fully connected artificial neural network (ANN). The algorithms were optimised on training data using five-fold cross validation. Subgroup analysis was performed using mean Shapley additive explanations (SHAP). Results: Accuracy scoring identified the ANN as the optimal predictor of metastasis (0.94), followed by: Logistic Regression Trainer (0.82), Random Forest Classifier (0.80), and Decision Tree Classifier (0.71). Preliminary subgroup analysis identified immunosuppression as most sensitive risk factor for developing metastatic disease (SHAP = 0.122). Conclusions: Significant heterogeneity in current morbidity and mortality data has limited the capacity of traditional statistical models and tumour staging systems to identify very high-risk cSSC. Our findings demonstrate that ML algorithms can accurately predict metastatic events in cSSC populations. Further development of a model user-interface is necessary to support the development of a useful risk stratification tool to guide clinical practice.
Using EHR data and machine learning approach to facilitate the identification of patients with lung cancer from a pan-cancer cohort.
Authors: Yue Yu, Kathryn Jean Ruddy, Konstantinos Leventakos, Bolun Liu, Nan Huo, Deirdre R. Pachman, Nansu Zong, Guohui Xiao, Christopher Chute, Emily Pfaff, Andrea L. Cheville, Guoqian Jiang
Journal: Journal of Clinical Oncology (2023)

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]
Radiotherapy combined with pyrotinib plus capecitabine in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer and brain metastases: A single-arm, exploratory trial.
Authors: Jing Wang, Ping Wang, Jinqiang You, Hailing Hou, Baozhong Zhang, Yanlan Chai, Yuanjie Cao, Bin Zhang, Xu Wang
Journal: Journal of Clinical Oncology (2023)

e13024 Background: Previous researches indicated the enhanced radiosensitivity in HER2-positive cancer cells by pyrotinib. For patients with treatment-naive brain metastases and central nervous system (CNS) symptoms, whether radiotherapy (current mainstay treatment) combined with pyrotinib-based systemic regimen is more beneficial remains unclear. This study explored radiotherapy combined with pyrotinib plus capecitabine in this population. Methods: In this exploratory trial (NCT04767828), patients aged 18-75 years who had HER2-positive metastatic breast cancer and untreated symptomatic brain metastases with an Eastern Cooperative Oncology Group performance status of 0-2 were included. Patients with meningeal metastases or without measurable CNS lesions were excluded. Eligible patients received whole-brain radiotherapy (WBRT) or stereotactic radiotherapy (SRT). The irradiation sites, total radiation dose, and fractions were decided by investigator based on the number of CNS lesions and size. Pyrotinib (400 mg, once a day) and capecitabine (1000 mg/m 2 , twice a day, on days 1-14 of each 21-day cycle) were given since the start of radiotherapy until disease progression or intolerable toxicity. The primary endpoint was CNS progression-free survival (PFS), assessed by Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM). Extracranial lesions were assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Results: Between May 5, 2020 and November 25, 2022, a total of 19 patients were enrolled. Thirteen (68%) patients had other visceral metastases, and 18 (95%) had prior trastuzumab treatment (5 for early breast cancer, 12 for advanced breast cancer, and 1 for both). Fifteen (79%) patients received WBRT and 4 (21%) received SRT. By the data cutoff date on January 16, 2023, the median follow-up duration was 15.0 months (range, 1.7-24.8). Median CNS PFS was 10.0 months (95% CI, 5.3-14.7). CNS objective response rate was 100%, including two (11%) patients with CNS complete response. Median overall PFS was 8.5 months (95% CI, 4.8-12.2). Median overall survival was 19.0 months (95% CI, 13.1-24.9). The most common grade 3 or 4 adverse events were vomiting (16%) and nausea (11%). For 11 patients with available results of Mini-Mental State Examination (MMSE), 10 (91%) had normal cognitive function during study period or recovered to normal level within 6 months after radiotherapy. Conclusions: Radiotherapy combined with pyrotinib plus capecitabine shows favorable CNS response and promising survival outcomes in patients with HER2-positive metastatic breast cancer and untreated symptomatic brain metastases, with an acceptable safety profile. This combination strategy warrants further validation in large samples. Clinical trial information: NCT04767828 .
Comprehensive molecular profiling from liquid and tissue for analysing the somatic mutational landscape in an Indian breast cancer cohort.
Authors: Nitesh Rohatgi, Sewanti Atul Limaye, Ankur Bahl, Bhawna Sirohi, Sidharth Sahni, Darshana Patil, Vineet Datta, Datar Limited, Tim Crook, Razelle Kurzrock, Massimo Cristofanilli
Journal: Journal of Clinical Oncology (2023)

e13080 Background: Breast cancer is a genetically and clinically heterogeneous entity, with high prevalence in the Indian population. The primary objective of this study was to characterize the somatic mutational landscape of metastatic breast cancer in Indian patients from tumor and liquid specimens. Methods: We performed molecular profiling of 341 tumor tissue specimens and 901 blood specimens from individual breast cancer patients, with paired tissue and blood processing in a sub cohort (n=101). Targeted NGS evaluating single nucleotide alterations, copy number alterations, and fusions was performed on these specimens. Results: In the cohort with tissue-based sequencing (n = 341), TP53 was the prevalently altered gene (55%). PIK3CA and ESR1 mutations were detected in 32% and 9%, respectively. BRCA1/2 alterations were detected in 10.8%, ARID1A in 7%, KEAP1 in 6%, NF1 in 6%, and ATR in 6%. ERBB2 amplification was observed in 10% of the cases. ESR1 gene fusions were detected in 3.7%. This is clinically relevant as along with other ESR1 resistance mutations, ESR1 fusions are also known as recurrent drivers of endocrine therapy resistance and are impervious to ER-targeted therapies. In a sub cohort comprising 255 specimens, TMB analysis showed a median TMB of 5 mut/Mb (range 1 - 88 mut/Mb), and 15.3% specimens with TMB-High (≥10 mut/mb). The ctDNA analysis (n = 901) revealed TP53 being the most altered (41%) gene, followed by PIK3CA (23%), ESR1(11%), EGFR (6%) and GNAS (4%). Acquired resistance mutations in ERBB2 were identified in ctDNA analysis liquid biopsy from two patients treated with HER2-targeted therapy and were absent in corresponding tissue samples. Mutations in ESR1 were more commonly seen in liquid biopsy than paired tissue, indicating this acquired resistance alteration being better captured in ctDNA than tissue due to tumor heterogeneity and tumor evolution. This indicates causative role for ESR1 mutations in endocrine resistance but not in disease pathogenesis. Conclusions: We report the largest liquid biopsy study reporting the genomic landscape from breast cancer cases of Indian origin along with tissue based molecular landscape. High concordance of liquid for PIK3CA alteration and higher sensitivity for detection of resistance conferring alterations like ESR1 and ERBB2 shows utility of liquid biopsy profiling. It reinforces the utility of liquid biopsy as effective tool that reveal clinically relevant information including mutational status and therapeutic options and seem more relevant in acquired resistance settings. [Table: see text]
Relation assessment between TAN and AR with the survival of breast cancer patients.
Authors: Minoosh Moghimi, Pouria Tavakkolian, Farzane Ahmadi, Reza Mansouri, Kasra Khodadadi
Journal: Journal of Clinical Oncology (2023)

e12542 Background: Considering the importance of the relationship between biomarkers and survival in breast cancer patients, finding this relationship can play a key role in understanding disease behavior, determining prognosis and even identifying targeted treatments. This study was conducted with the aim of investigating the relationship between TAN (Tumor associated neutrophil) and AR (Androgen receptor) with survival in patients with breast cancer. Methods: In this cohort study, 83 female patients with stage I- IV breast cancer were included. we investigated the relationship between TAN and AR with the three-year survival time. Patients were diagnosed between April 2018 and March 2021. Also, the latest follow-up was performed in December 2022. The Cox-hazard model was used to determine the factors influencing the survival time of patients. Results: Out of 83, follow-up was conducted for 81 women. The mean±SD age of diagnosis was 49.5±11.1 years. Sixteen (19.75%) of them died and the mean (95% C.I) for follow-up time was 32.78 (30.97-34.58) months. The one, two, and three survival rates were 0.95, 0.89, and 0.80, respectively. The mean survival time (month) for AR-TAN-, AR+TAN-, AR+TAN+, and AR-TAN+ was 35.75, 33.50, 32.36, and 28.58 respectively. The 3-year survival in the AR-TAN-, AR+TAN-, AR+TAN+ and AR-TAN+ groups was 0.88, 0.88, 0.79, and 0.50, respectively. Conclusions: AR positivity and TAN negativity are in favor of better three-year overall survival regardless of subtypes, however the differences were not statistically significant. The Survival status was significantly lower in the AR-TAN+ group compared to AR + TAN- group. These results are a promising way to the future studies. [Table: see text]
Olaparib (O) + ceralasertib (C) in patients (pts) with metastatic triple-negative breast cancer (mTNBC): Translational analysis of the VIOLETTE trial.
Authors: Natalia Lukashchuk, Kevin Punie, Zbigniew Nowecki, Seock-Ah Im, Anne C. Armstrong, William Jacot, Jee Hyun Kim, Marc A. Webster, Judith Balmaña, Suzette Delaloge, Ed Casson, Bienvenu Loembe, Emma Dean, Joshua Armenia, Andrew NJ Tutt
Journal: Journal of Clinical Oncology (2023)

1087 Background: O is a PARP inhibitor approved in a number of tumor types and for use in pts with germline (g) mutations in BRCA ( BRCAm) HER2-negative metastatic breast cancer. C is an inhibitor of DDR kinase ATR. Analysis of VIOLETTE (NCT03330847) in pts with mTNBC showed no improvement in PFS with a combination of C and O vs. O, whether in the context of pathogenic BRCAm, non-BRCA HRR pathway mutations (HRRm), or homologous recombination repair (HRR) wildtype (HRRwt) tumors. We report an exploratory translational analysis of predictive genomic biomarkers of response to O ± C beyond g BRCAm. Methods: Pts received O (300 mg twice daily) ± C (160 mg daily; days 1–7; 28-day cycles) as a second- or third-line treatment option. Pts were prospectively stratified into molecular strata based on pathogenic/likely pathogenic alterations in BRCA1/2 ( BRCAm; n=83) or in ≥1 of 13 selected non- BRCA HRR genes (HRRm; n=40) or HRRwt (n=103) using FoundationOneCDx (F1CDx) sequencing assay of archival tumor sample. Advanced genomic analysis included genome-wide loss of heterozygosity (a marker of HRR deficiency [HRD] with a 16% cut-off to define HRD+ [≥16%] vs HRD− [&lt;16%]), zygosity, and predicted g/somatic (s) status of BRCA and HRR alterations based on a validated computational FMI algorithm. Efficacy was evaluated by blinded independent central review of PFS, RECIST response, and best percentage change from baseline in target lesion size. Results: Out of evaluable pts for origin of BRCAm (n=42), 81% (n=34) were of germline, and 19% (n=8) were of somatic origin. Of pts evaluable for zygosity (n=48), most BRCA alterations were biallelic (90%; n=43), and of pts evaluable for HRD status (n=47), most were HRD+ (94%; n=44). Of HRRwt pts evaluable for HRD status (n=79), 57% were HRD+ (n=45). Responses by biomarker are shown. Responses in BRCAm pts were seen in all subgroups (zygosity, HRD, or g/s origin of mutation), including s BRCAm (ORR: n=3/4 on O; n=2/4 on O+C). Responses were also seen in patients with heterozygous BRCA alteration (n=3/5) by genomic result and in BRCAm/HRD− pts (n=3/3) across both arms. Pts with select HRR genes ( BARD1, RAD51C/D, ATM, CDK12) also achieved responses on O or O+C. Conclusions: Responses to O were seen in pts with both g BRCAm and s BRCAm. Beyond BRCAm, the efficacy of O was observed in pts with alterations in select HRR genes, such as BARD1 or RAD51C/D; further clinical investigation is required. Small sample size and HRD analysis performed on archival tumor samples should be considered limitations. Clinical trial information: NCT03330847 . [Table: see text]